Biostat 203B Homework 2

Due Feb 9 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.2 compiler_4.3.2    fastmap_1.1.1     cli_3.6.1        
 [5] tools_4.3.2       htmltools_0.5.7   rstudioapi_0.15.0 yaml_2.3.7       
 [9] rmarkdown_2.25    knitr_1.45        jsonlite_1.8.7    xfun_0.41        
[13] digest_0.6.33     rlang_1.1.1       evaluate_0.23    

Load necessary libraries (you can add more as needed).

library(arrow)
library(data.table)
library(memuse)
library(pryr)
library(R.utils)
library(tidyverse)
library(fs)

Display memory information of your computer

memuse::Sys.meminfo()

Totalram:  16.000 GiB 
Freeram:    3.508 GiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/

total 8859752
-rw-rw-r--@ 1 hanbeixiong  staff    15516088 Jan  5  2023 admissions.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff      427468 Jan  5  2023 d_hcpcs.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff      859438 Jan  5  2023 d_icd_diagnoses.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff      578517 Jan  5  2023 d_icd_procedures.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff       12900 Jan  5  2023 d_labitems.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    25070720 Jan  5  2023 diagnoses_icd.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     7426955 Jan  5  2023 drgcodes.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   508524623 Jan  5  2023 emar.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   471096030 Jan  5  2023 emar_detail.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     1767138 Jan  5  2023 hcpcsevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff  1939088924 Jan  5  2023 labevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    96698496 Jan  5  2023 microbiologyevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    36124944 Jan  5  2023 omr.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     2312631 Jan  5  2023 patients.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   398753125 Jan  5  2023 pharmacy.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   498505135 Jan  5  2023 poe.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    25477219 Jan  5  2023 poe_detail.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   458817415 Jan  5  2023 prescriptions.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     6027067 Jan  5  2023 procedures_icd.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff      122507 Jan  5  2023 provider.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     6781247 Jan  5  2023 services.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    36158338 Jan  5  2023 transfers.csv.gz

ls -l ~/mimic/icu/

total 6155968
-rw-rw-r--@ 1 hanbeixiong  staff       35893 Jan  5  2023 caregiver.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff  2467761053 Jan  5  2023 chartevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff       57476 Jan  5  2023 d_items.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    45721062 Jan  5  2023 datetimeevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff     2614571 Jan  5  2023 icustays.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   251962313 Jan  5  2023 ingredientevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff   324218488 Jan  5  2023 inputevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    38747895 Jan  5  2023 outputevents.csv.gz
-rw-rw-r--@ 1 hanbeixiong  staff    20717852 Jan  5  2023 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage.)

Answer:

‘fread’ is the fastest. The default parsed data types are not completely the same. Some variables have different data types while passing into different functions. For example, all the time related variables are passed in as character type using ‘read.csv’ but the other two functions read these time variables as type ‘POSIct’ which is a type represented the time. For some other variables like ‘subject_id’, ‘hadm_id’ and ‘hospital_expire_flag’, both ‘read.csv’ and ‘fread’ read them as integer but ‘read_csv’ read them as number. The resultant dataframe by ‘read.csv’ uses 158.71 MB memory. The resultant tipple by ‘read_csv’ uses 55.31 MB memory. The resultant dataframe by ‘fread’ uses 50.13 MB memory.

system.time(df1 <- read.csv("~/mimic/hosp/admissions.csv.gz"))

   user  system elapsed 
  3.773   0.072   3.846 

str(df1)

'data.frame':   431231 obs. of  16 variables:
 $ subject_id          : int  10000032 10000032 10000032 10000032 10000068 10000084 10000084 10000108 10000117 10000117 ...
 $ hadm_id             : int  22595853 22841357 25742920 29079034 25022803 23052089 29888819 27250926 22927623 27988844 ...
 $ admittime           : chr  "2180-05-06 22:23:00" "2180-06-26 18:27:00" "2180-08-05 23:44:00" "2180-07-23 12:35:00" ...
 $ dischtime           : chr  "2180-05-07 17:15:00" "2180-06-27 18:49:00" "2180-08-07 17:50:00" "2180-07-25 17:55:00" ...
 $ deathtime           : chr  "" "" "" "" ...
 $ admission_type      : chr  "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr  "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr  "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr  "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr  "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr  "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr  "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr  "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : chr  "2180-05-06 19:17:00" "2180-06-26 15:54:00" "2180-08-05 20:58:00" "2180-07-23 05:54:00" ...
 $ edouttime           : chr  "2180-05-06 23:30:00" "2180-06-26 21:31:00" "2180-08-06 01:44:00" "2180-07-23 14:00:00" ...
 $ hospital_expire_flag: int  0 0 0 0 0 0 0 0 0 0 ...

pryr::object_size(df1)

158.71 MB

system.time(df2 <- read_csv("~/mimic/hosp/admissions.csv.gz"))

   user  system elapsed 
  1.488   1.477   0.721 

str(df2)

spc_tbl_ [431,231 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ subject_id          : num [1:431231] 1e+07 1e+07 1e+07 1e+07 1e+07 ...
 $ hadm_id             : num [1:431231] 22595853 22841357 25742920 29079034 25022803 ...
 $ admittime           : POSIXct[1:431231], format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct[1:431231], format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct[1:431231], format: NA NA ...
 $ admission_type      : chr [1:431231] "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr [1:431231] "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr [1:431231] "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr [1:431231] "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr [1:431231] "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr [1:431231] "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr [1:431231] "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr [1:431231] "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct[1:431231], format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct[1:431231], format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: num [1:431231] 0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "spec")=
  .. cols(
  ..   subject_id = col_double(),
  ..   hadm_id = col_double(),
  ..   admittime = col_datetime(format = ""),
  ..   dischtime = col_datetime(format = ""),
  ..   deathtime = col_datetime(format = ""),
  ..   admission_type = col_character(),
  ..   admit_provider_id = col_character(),
  ..   admission_location = col_character(),
  ..   discharge_location = col_character(),
  ..   insurance = col_character(),
  ..   language = col_character(),
  ..   marital_status = col_character(),
  ..   race = col_character(),
  ..   edregtime = col_datetime(format = ""),
  ..   edouttime = col_datetime(format = ""),
  ..   hospital_expire_flag = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

pryr::object_size(df2)

55.31 MB

system.time(df3 <- fread("~/mimic/hosp/admissions.csv.gz"))

   user  system elapsed 
  0.472   0.122   0.596 

str(df3)

Classes 'data.table' and 'data.frame':  431231 obs. of  16 variables:
 $ subject_id          : int  10000032 10000032 10000032 10000032 10000068 10000084 10000084 10000108 10000117 10000117 ...
 $ hadm_id             : int  22595853 22841357 25742920 29079034 25022803 23052089 29888819 27250926 22927623 27988844 ...
 $ admittime           : POSIXct, format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct, format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct, format: NA NA ...
 $ admission_type      : chr  "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr  "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr  "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr  "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr  "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr  "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr  "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr  "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct, format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct, format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: int  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, ".internal.selfref")=<externalptr> 

pryr::object_size(df3)

50.13 MB

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv.)

Answer: The runtime is faster. It takes 0.903 second compared to 1.016 seconds which is calling without specifying column data types. The resultant tibble uses 79.46 MB memory.

system.time(df2 <- read_csv("~/mimic/hosp/admissions.csv.gz",
                            col_types='icTTTccccccccTTi'))

   user  system elapsed 
  1.652   1.490   0.857 

pryr::object_size(df2)

79.46 MB

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 125x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

-rw-rw-r--@ 1 hanbeixiong  staff  1939088924 Jan  5  2023 /Users/hanbeixiong/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,45421181,51237,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,1.4,1.4,,0.9,1.1,abnormal,ROUTINE,
2,10000032,,45421181,51274,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,___,15.1,sec,9.4,12.5,abnormal,ROUTINE,VERIFIED.
3,10000032,,52958335,50853,P28Z0X,2180-03-23 11:51:00,2180-03-25 11:06:00,___,15,ng/mL,30,60,abnormal,ROUTINE,NEW ASSAY IN USE ___: DETECTS D2 AND D3 25-OH ACCURATELY.
4,10000032,,52958335,50861,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,102,102,IU/L,0,40,abnormal,ROUTINE,
5,10000032,,52958335,50862,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,3.3,3.3,g/dL,3.5,5.2,abnormal,ROUTINE,
6,10000032,,52958335,50863,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,109,109,IU/L,35,105,abnormal,ROUTINE,
7,10000032,,52958335,50864,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,___,8,ng/mL,0,8.7,,ROUTINE,MEASURED BY ___.
8,10000032,,52958335,50868,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,12,12,mEq/L,8,20,,ROUTINE,
9,10000032,,52958335,50878,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,143,143,IU/L,0,40,abnormal,ROUTINE,

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 5 minutes on your computer, then abort the program and report your findings.

df <- read_csv("~/mimic/hosp/labevents.csv.gz")

Answer: I got the error “Error: vector memory exhausted (limit reached?)” which indicate that the memory in my local machine is not enough to read the file.

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

df <- read_csv("~/mimic/hosp/labevents.csv.gz", col_select=c('subject_id', 
                                                             'itemid', 
                                                             'charttime', 
                                                             'valuenum'))

Answer: This did not solve the issue. I still got the error “Error: vector memory exhausted (limit reached?)” which indicate that the memory is not enough to read the file.

Q2.3 Ingest subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. To save render time, put #| eval: false at the beginning of this code chunk.)

#Get an idea of the information of the file which will be reading in next chunk
zcat < ~/mimic/hosp/labevents.csv.gz | head -5

zcat < ~/mimic/hosp/labevents.csv.gz | awk -F, 'BEGIN{OFS=","} {if($5==50912 || 
$5==50971 || $5==50983 || $5==50902 || $5==50882 || $5==51221 || $5==51301 || 
$5==50931) print $2,$5,$7,$10}' | gzip > ./labevents_filtered.csv.gz

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Answer: There are 24,855,909 lines in this new file. It takes 9.097 seconds to ingest labevents_filtered.csv.gz by read_csv.

This code chunk is showing the first 10 lines of the new file.

zcat < ./labevents_filtered.csv.gz | head -10

10000032,50882,2180-03-23 11:51:00,27
10000032,50902,2180-03-23 11:51:00,101
10000032,50912,2180-03-23 11:51:00,0.4
10000032,50971,2180-03-23 11:51:00,3.7
10000032,50983,2180-03-23 11:51:00,136
10000032,50931,2180-03-23 11:51:00,95
10000032,51221,2180-03-23 11:51:00,45.4
10000032,51301,2180-03-23 11:51:00,3
10000032,51221,2180-05-06 22:25:00,42.6
10000032,51301,2180-05-06 22:25:00,5

This code chuck displayed the number of lines in the new file.

zcat < ./labevents_filtered.csv.gz | wc -l

 24855909

This code chunk is showing the time it takes to ingest labevents_filtered.csv.gz by read_csv.

system.time(read_csv("./labevents_filtered.csv.gz"))

   user  system elapsed 
 22.390  23.260   8.078 

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory. To save render time, put #| eval: false at the beginning of this code chunk.

gzip -dk < ~/mimic/hosp/labevents.csv.gz > ./labevents.csv

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Answer: It takes 0.019 seconds in ingest+select+filter process. There are 24855909 rows. The first 10 rows matches with those in Q2.3.

This code chunk is showing the amount of time for the operation required in the question:

system.time(df <- arrow::open_dataset('./labevents.csv', format = "csv") |> 
              dplyr::select(subject_id, itemid, charttime, valuenum) |> 
              dplyr::filter(itemid %in% c(50912, 50971, 50983, 50902, 50882, 
                                          51221, 51301, 50931)))

   user  system elapsed 
  0.050   0.006   0.054 

df <- as_tibble(df)

Here is count of tipple:

count(df)

# A tibble: 1 × 1
         n
     <int>
1 24855909

Here is first 10 rows of tipple:

head(df, 10)

# A tibble: 10 × 4
   subject_id itemid charttime           valuenum
        <int>  <int> <dttm>                 <dbl>
 1   10000032  50882 2180-03-23 04:51:00     27  
 2   10000032  50902 2180-03-23 04:51:00    101  
 3   10000032  50912 2180-03-23 04:51:00      0.4
 4   10000032  50971 2180-03-23 04:51:00      3.7
 5   10000032  50983 2180-03-23 04:51:00    136  
 6   10000032  50931 2180-03-23 04:51:00     95  
 7   10000032  51221 2180-03-23 04:51:00     45.4
 8   10000032  51301 2180-03-23 04:51:00      3  
 9   10000032  51221 2180-05-06 15:25:00     42.6
10   10000032  51301 2180-05-06 15:25:00      5  

Write a few sentences to explain what is Apache Arrow. Imagine you want to explain it to a layman in an elevator.

Answer: Apache Arrow is a bit like a universal translator for data. It’s a tool that helps different computer systems and programming languages understand and work with large amounts of data quickly and efficiently.

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Answer: The Parquet file is about 2.07 GB. It takes 0.164 seconds to ingest+select+filter the Parquet file. The number of rows is 24855909. The first 10 rows of the result tibble match those in Q2.3. Here are codes to generate these results.

arrow::write_dataset(arrow::open_dataset('./labevents.csv', format = "csv"), 
                     './labevents.parquet')

Here contains the size of the file:

ls -l labevents.parquet

total 4042568
-rw-r--r--  1 hanbeixiong  staff  2069791662 Feb  7 17:18 part-0.parquet

# Check ingest+select+filter time
system.time(df <- arrow::open_dataset('./labevents.parquet') |> 
              dplyr::select(subject_id, itemid, charttime, valuenum) |> 
              dplyr::filter(itemid %in% c(50912, 50971, 50983, 50902, 50882, 
                                          51221, 51301, 50931)))

   user  system elapsed 
  0.133   0.020   0.156 

df <- as_tibble(df)
df <- arrange(df, subject_id, charttime)

Here is count of tipple:

count(df)

# A tibble: 1 × 1
         n
     <int>
1 24855909

Here is first 10 rows of tipple:

head(df,10)

# A tibble: 10 × 4
   subject_id itemid charttime           valuenum
        <int>  <int> <dttm>                 <dbl>
 1   10000032  50882 2180-03-23 04:51:00     27  
 2   10000032  50902 2180-03-23 04:51:00    101  
 3   10000032  50912 2180-03-23 04:51:00      0.4
 4   10000032  50971 2180-03-23 04:51:00      3.7
 5   10000032  50983 2180-03-23 04:51:00    136  
 6   10000032  50931 2180-03-23 04:51:00     95  
 7   10000032  51221 2180-03-23 04:51:00     45.4
 8   10000032  51301 2180-03-23 04:51:00      3  
 9   10000032  51221 2180-05-06 15:25:00     42.6
10   10000032  51301 2180-05-06 15:25:00      5  

Write a few sentences to explain what is the Parquet format. Imagine you want to explain it to a layman in an elevator.

Answer: Parquet is a file format designed for efficient data storage and retrieval. It compresses and encodes complex and big data into a format which can be saved and used efficiently in data analysis.

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Answer: It takes 0.202 seconds to ingest+convert+select+filter the Parquet file. The number of rows is 24855909. The first 10 rows after sorting the result tibble will match those in Q2.3. However, it might not match exactly if I do not sort here. Parallel processing causes uncertainty in output which sometimes change the order of rows. Here are codes to generate these results.

system.time(df <- arrow::open_dataset('./labevents.parquet') |> 
              arrow::to_duckdb(table_name = "labevents_table") |> 
              dplyr::select(subject_id, itemid, charttime, valuenum) |> 
              dplyr::filter(itemid %in% c(50912, 50971, 50983, 50902, 50882, 
                                          51221, 51301, 50931)))

   user  system elapsed 
  0.303   0.033   0.373 

df <- as_tibble(df)
df <- arrange(df, subject_id, charttime)

Here is count of tipple:

count(df)

# A tibble: 1 × 1
         n
     <int>
1 24855909

Here is first 10 rows of tipple:

head(df, 10)

# A tibble: 10 × 4
   subject_id itemid charttime           valuenum
        <dbl>  <dbl> <dttm>                 <dbl>
 1   10000032  50882 2180-03-23 11:51:00     27  
 2   10000032  50902 2180-03-23 11:51:00    101  
 3   10000032  50912 2180-03-23 11:51:00      0.4
 4   10000032  50971 2180-03-23 11:51:00      3.7
 5   10000032  50983 2180-03-23 11:51:00    136  
 6   10000032  50931 2180-03-23 11:51:00     95  
 7   10000032  51221 2180-03-23 11:51:00     45.4
 8   10000032  51301 2180-03-23 11:51:00      3  
 9   10000032  51221 2180-05-06 22:25:00     42.6
10   10000032  51301 2180-05-06 22:25:00      5  

Write a few sentences to explain what is DuckDB. Imagine you want to explain it to a layman in an elevator.

Answer: DuckDB is a database management system that is designed to be fast and efficient. It’s like a super organized library for data, where you can quickly find and retrieve the information you need.

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220179,82,82,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220180,59,59,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220181,63,63,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220045,94,94,bpm,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220179,85,85,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220180,55,55,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220181,62,62,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220210,20,20,insp/min,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220277,95,95,%,0

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.

Answer:

We first unzip the file to current directory

gzip -dk < ~/mimic/icu/chartevents.csv.gz > ./chartevents.csv

We then convert the unzipped csv file to parquet format

arrow::write_dataset(arrow::open_dataset('./chartevents.csv', format = "csv"), 
                     './chartevents.parquet')

Create a subset of data based on the itemid

df <- arrow::open_dataset('./chartevents.parquet') |> 
  dplyr::filter(itemid %in% c(220045, 220181, 220179, 223761, 220210))

Convert data format to tipple and sort it for reproducibility

df <- as_tibble(df)
df <- arrange(df, subject_id, charttime)

Count number of rows:

count(df)

# A tibble: 1 × 1
         n
     <int>
1 22502319

Display the first 10 rows:

head(df, 10)

# A tibble: 10 × 11
   subject_id  hadm_id  stay_id caregiver_id charttime          
        <int>    <int>    <int>        <int> <dttm>             
 1   10000032 29079034 39553978        88981 2180-07-23 07:00:00
 2   10000032 29079034 39553978        88981 2180-07-23 07:11:00
 3   10000032 29079034 39553978        88981 2180-07-23 07:11:00
 4   10000032 29079034 39553978        88981 2180-07-23 07:12:00
 5   10000032 29079034 39553978        88981 2180-07-23 07:12:00
 6   10000032 29079034 39553978        88981 2180-07-23 07:30:00
 7   10000032 29079034 39553978        88981 2180-07-23 07:30:00
 8   10000032 29079034 39553978        88981 2180-07-23 07:30:00
 9   10000032 29079034 39553978        88981 2180-07-23 07:30:00
10   10000032 29079034 39553978        88981 2180-07-23 08:00:00
# ℹ 6 more variables: storetime <dttm>, itemid <int>, value <chr>,
#   valuenum <dbl>, valueuom <chr>, warning <int>